Year: 2010
Type: conference-paper
Abstract: Segmentation of novel or dynamic objects in a scene, often referred to as background sub-traction or foreground segmentation, is critical for robust high level computer vision applica-tions such as object tracking, object classifica-tion and recognition. However, automatic real-time segmentation for robotics still poses chal-lenges including global illumination changes, shadows, inter-reflections, colour similarity of foreground to background, and cluttered back-grounds. This paper introduces depth cues provided by structure from motion (SFM) for interactive segmentation to alleviate some of these challenges. In this paper, two prevailing interactive segmentation algorithms are com-pared; Lazysnapping [Li et al., 2004] and Grab-cut [Rother et al., 2004], both based on graph-cut optimisation [Boykov and Jolly, 2001]. The algorithms are extended to include depth cues rather than colour only as in the original pa-pers. Results show interactive segmentation based on colour and depth cues enhances the performance of segmentation with a lower er-ror with respect to ground truth. 1
Authors: Hu He, David McKinnon, Michael Warren, Ben Upcroft
Language: English
Subfield: Computer Vision and Pattern Recognition
Field: Computer Science
Domain: Physical Sciences
Open Access status: green
Funders: CRCMining, China Scholarship Council
OpenAlex ID: W144883078